Python Pandas：每月或每周拆分一个 TimeSerie

Python Pandas: Split a TimeSerie per month or week

提问by Radar

I have a Timeserie that spans few year, in the following format:

我有一个跨越几年的 Timeserie，格式如下：

              timestamp open    high    low    close    volume
0   2009-01-02 05:00:00 900.00  906.75  898.00  904.75  15673.0
1   2009-01-02 05:30:00 904.75  907.75  903.75  905.50  4600.0
2   2009-01-02 06:00:00 905.50  907.25  904.50  904.50  3472.0
3   2009-01-02 06:30:00 904.50  905.00  903.25  904.75  6074.0
4   2009-01-02 07:00:00 904.75  905.50  897.00  898.25  12538.0

What would be the simplest way to split that dataframe into multiple dataframes of 1 week or 1 month worth of data?77

将该数据帧拆分为 1 周或 1 个月数据的多个数据帧的最简单方法是什么？77

EDIT: as an example a dataframe containing 1 year of data would be split in 52 dataframes containing a week of data and returned as a list of 52 dataframes

编辑：例如，包含 1 年数据的数据帧将被拆分为包含一周数据的 52 个数据帧，并作为 52 个数据帧的列表返回

(the data can be reconstructed with the formula below)

（数据可以用下面的公式重构）

import pandas as pd
from pandas import Timestamp
dikt={'close': {0: 904.75, 1: 905.5, 2: 904.5, 3: 904.75, 4: 898.25}, 'low': {0: 898.0, 1: 903.75, 2: 904.5, 3: 903.25, 4: 897.0}, 'open': {0: 900.0, 1: 904.75, 2: 905.5, 3: 904.5, 4: 904.75}, 'high': {0: 906.75, 1: 907.75, 2: 907.25, 3: 905.0, 4: 905.5}, 'volume': {0: 15673.0, 1: 4600.0, 2: 3472.0, 3: 6074.0, 4: 12538.0}, 'timestamp': {0: Timestamp('2009-01-02 05:00:00'), 1: Timestamp('2009-01-02 05:30:00'), 2: Timestamp('2009-01-02 06:00:00'), 3: Timestamp('2009-01-02 06:30:00'), 4: Timestamp('2009-01-02 07:00:00')}}
pd.DataFrame(dikt, columns=['timestamp', 'open', 'high', 'low', 'close', 'volume'])

采纳答案by piRSquared

use groupbywith pd.TimeGrouperand list comprehensions

使用groupby与pd.TimeGrouper和list解析

weeks = [g for n, g in df.set_index('timestamp').groupby(pd.TimeGrouper('W'))]
months = [g for n, g in df.set_index('timestamp').groupby(pd.TimeGrouper('M'))]

---

---

You can reset the index if you need

如果需要，您可以重置索引

weeks = [g.reset_index()
         for n, g in df.set_index('timestamp').groupby(pd.TimeGrouper('W'))]
months = [g.reset_index()
          for n, g in df.set_index('timestamp').groupby(pd.TimeGrouper('M'))]

---

---

in a dict

在一个 dict

weeks = {n: g.reset_index()
         for n, g in df.set_index('timestamp').groupby(pd.TimeGrouper('W'))}
months = {n: g.reset_index()
          for n, g in df.set_index('timestamp').groupby(pd.TimeGrouper('M'))}

回答by toto_tico

The pd.TimeGrouperis deprecatedand will be removed, you can use pd.Grouperinstead.

将pd.TimeGrouper被弃用，并且将被删除，您可以使用pd.Grouper来代替。

weeks = [g for n, g in df.groupby(pd.Grouper(key='timestamp',freq='W'))]
months = [g for n, g in df.groupby(pd.Grouper(key='timestamp',freq='M'))]

This way you can also avoid setting the timestampas index.

这样你也可以避免设置timestampas index。

---

---

Also, if your timestamp is part of a multi index, you can refer to it using using the levelparameter (e.g. pd.Grouper(level='timestamp', freq='W')). Than @jtromans for the heads up.

此外，如果您的时间戳是多索引的一部分，您可以使用level参数（例如pd.Grouper(level='timestamp', freq='W')）来引用它。比@jtromans 提神。

回答by rtkaleta

Convert the timestampcolumn into DateTimeIndex, then you can slice into it in a variety of ways.

将timestamp列转换为DateTimeIndex，然后您可以通过多种方式对其进行切片。

回答by coredump

I would use group by for this, assume df stores the data

我会为此使用 group by，假设 df 存储数据

df = df.set_index('timestamp')
df.groupby(pd.TimeGrouper(freq='D'))

then resulting groups would contain all the dataframes you are looking for. this answer is referenced here

那么结果组将包含您正在寻找的所有数据帧。此处引用了此答案

How to group DataFrame by a period of time?

如何按一段时间对DataFrame进行分组？